Abstract Algorithms and frameworks for distributed machine learning have been widely used in numerous artificial intelligence engineering applications. A cloud platform provides a large number of resources at lower cost is more convenient method such With the rapid development containerization, native combinations based on Docker Kubernetes provided effective resource support learning. However,...

The real-life assembly production often has transportation between fabrication and assembly, the capacity of machine is considered; however, previous works are mainly about two-stage distributed scheduling problems. In this study, a energy-efficient problem (DEASP) with investigated, in which dedicated parallel machines symmetry under given conditions, an used. An adaptive imperialist competiti...

For the Just-in-Time production scheme in large manufacturing systems we describe a considerable methodological extension into an integrated automated manufacturing system that is supported by a distributed real-time computer system. This allows for higher flexibility in demand fluctuation as well as for larger variety of products. At the same time better adaptability of transportation planning...

A distributed problem solving approach t o j o b shop scheduling is described in this paper. The approach views the system as an Organisation. Agents are assigned different roles and functions depending on their position within the structure of the Organisation. In this Organisation, agents of the same level state their interests independently of each other and therefore Conflict is likely to o...

Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of distributed computing resources. Workflow scheduling is one of the key issues in the management of workflow execution. The scheduling process maps and manages the execution of the inter-dependent tasks on the distributed resources. Scheduling of workflows is a challenging task especially when many w...

We propose a new process scheduling queue system called the Distributed Queue Tree (DQT) for a distributed memory, dynamically partitionable parallel machines. We assume that partitions can be nested dynamically and that a process in a partition can be preempted. The combination of dynamically nested partitioning and time-sharing scheduling may provide an interactive environment and higher proc...

One of the major challenges in ultrascale systems is the effective scheduling of complex jobs within strict timing constraints. The distributed and heterogeneous system resources constitute another critical issue that must be addressed by the employed scheduling strategy. In this paper, we investigate by simulation the performance of various policies for the scheduling of real-time directed acy...

In this paper we take a look at three decades of the history and evolution of distributed data processing systems. We consider how allocation of resources to tasks, i.e. scheduling, is done in three systems and study how system goals influence scheduler design and possible scheduling optimizations. We start by examining the first general-purpose distributed computing system Condor [1], continue...